Unsupervised approach for building non-parametric background and foreground models of scenes with significant foreground activity
Bibliographic record
Abstract
Kernel-based density estimation have been successful for background subtraction in complex environments where background statistics at the pixel level cannot be described parametrically. These methods, however, typically requires a training sequence free or mostly free of foreground activity in order to get a good initial estimate of the background distribution. We present an approach for non-parametric statistical modeling of both foreground and background in complex and busy environments without any restrictions or constraints on the scene foreground activity at initialization. Our unsupervised approach uses the difference in relative frequency and probability mass between background and foreground modes to generate foreground and background likelihood functions as well as estimates of foreground and background priors. For each frame, the output is a non-binary mask of foreground probabilities which can be easily combined with spatial and temporal constraints in an intelligent decision process. Results show that our approach performs well in a variety of complex scenarios where foreground probabilities can be as high as 80%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".